Enhanced methodology for disaggregating space heating and domestic hot water heat loads of buildings in district heating networks

This paper presents an innovative approach to disaggregate a building's global heat consumption into space heating and domestic hot water heat load components using Energy Signature Curve models. The study addresses the challenges associated with these models, which often fail to represent daily trends accurately and do not account for dynamic changes in building usage. Four approaches based on linear regression models are compared to determine the most accurate method for space heating and domestic hot water disaggregation. The state-of-the-art Energy Signature Curve is compared with three improved alternatives. A new algorithm for automatic season threshold identification is proposed. The comparison with consumption data indicates that the proposed methodology significantly improves the accuracy in heat load disaggregation, with the superior performance provided by the model based on a 24-hour energy threshold. This advancement can potentially optimize district heating network management and support retrofit interventions by providing detailed consumption profiles.